{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Multiple Subplots"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Sometimes it is helpful to compare different views of data side by side.\n",
    "To this end, Matplotlib has the concept of *subplots*: groups of smaller axes that can exist together within a single figure.\n",
    "These subplots might be insets, grids of plots, or other more complicated layouts.\n",
    "In this section we'll explore four routines for creating subplots in Matplotlib."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "plt.style.use('seaborn-white')\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## ``plt.axes``: Subplots by Hand\n",
    "\n",
    "The most basic method of creating an axes is to use the ``plt.axes`` function.\n",
    "As we've seen previously, by default this creates a standard axes object that fills the entire figure.\n",
    "``plt.axes`` also takes an optional argument that is a list of four numbers in the figure coordinate system.\n",
    "These numbers represent ``[left, bottom, width, height]`` in the figure coordinate system, which ranges from 0 at the bottom left of the figure to 1 at the top right of the figure.\n",
    "\n",
    "For example, we might create an inset axes at the top-right corner of another axes by setting the *x* and *y* position to 0.65 (that is, starting at 65% of the width and 65% of the height of the figure) and the *x* and *y* extents to 0.2 (that is, the size of the axes is 20% of the width and 20% of the height of the figure):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f5c306af438>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax1 = plt.axes()  # standard axes\n",
    "ax2 = plt.axes([0.65, 0.65, 0.2, 0.2])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The equivalent of this command within the object-oriented interface is ``fig.add_axes()``. Let's use this to create two vertically stacked axes:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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jCQIgCl/QU5bmcr54Noye/m5M3ZTChmM5Ukd6IFU1tYxfdZztKXm891hrXn5Y\nlL2gP0ThC3rLytyMr6PbE+7biCkbkth8MlfqSPdUWV3LyyuPs/tUPh88FWgUN50F4yIKX9Br1hZm\nLB3ZgS4+rkyKO6m3pV9ZXcuLPxzl19MFzH4miJFdm0sdSRD+RhS+oPdsLM34dlQHOjRXMHHtSWZv\nT9erh7NKKqt54bsj/JFZyLzIYKI6N5M6kiDclSh8wSDYWpqzYnQnort4s2T/eUYsPUx+SaXUsTiW\ndY0Bnx3g8IWrfDwkhCEdmkodSRD+kSh8wWBYmZvx4dNt+WxYKCm5xTy26AAHzxXe/41aUFunZtHe\nDIZ8fWvXt3Vju/JMO7E+lKDfROELBuep0CZsfjUcRxsLnv3mMIv3Zep0E5Xc6xUMX3KIj3ef5fFg\nT7ZP7E57bxedHV8Q/i1R+IJB8nd3YMurDzEgyJP5u84w5oejXCzUzi5Bf7U95QqPfrqftMvFfDwk\nhM+GtcPR2kLrxxUETRBP2goGy97KnNjh7ejg7cLsHafpvfA3ngptwiu9WtJSaa+x46jVao5mXeOb\nA+fZlZZPSFNnFg0LFZuOCwanXoVfVVXF9OnTycjIYNOmTX97vbS0lMmTJ1NaWoqtrS0LFy7E2dlZ\nY2EF4X/JZDJGhfswIMiTJfvPs+rwJX46mcuAIE9e7dWS1p7/fkOVqppatiZdYXn8BdIul+BkY8Hr\nffx4pVdLLMzEh2PB8NSr8OfNm0fr1q3JyMi46+vff/89nTp1YsyYMcTFxbF06VKmTJmikaCCcC9K\nR2vee7wNLz/sy7d/XOCHhCy2JV+hbxt3nmnXhNaejngrbB9oHfq84kpWJ15i9eEsCstu4qe0Z9Yz\nbXmmXROx/6xg0Or12ztp0iSuX7/Oli1b7vp6QkICs2fPBqBXr16MGzeu4QkFoR5c7a14q38AY3v4\n8t3BiyyLv8DuU/kA2Fqa0crDgdaejrT2dMTdwYrL1yvIuVZB9rVysq9WkHOtnJLKGmQy6N1KyfPh\nPoS3dEUmExuWCIavXoVvb2/P9evX//H1wsJCFAoFAK6urhQUFDQsnSD8S062Fkzs48fYni3IyC8j\n/UoJp66UkH6lhJ+TLrP68H8XZLO2kNPUxRYvFxs6NHehqYstfdu407yRGKMXjIvWPp/qcKtcQfhH\n1hZmBHk5EeT137Xo1Wo1l4srKSytoomLDa52luIKXjAJ9y381atXs2PHDlxcXFi0aNE9v1epVKJS\nqXBwcCAjs1QfAAAgAElEQVQ/Px+lUqmxoIKgKTKZjCbONjRxtpE6iiDo1H0LPyoqiqioqAf6YeHh\n4ezcuZPx48fzyy+/0L179wYHFARBEDSjXnPLJkyYwBtvvMGFCxeIjo5m69atqFQqpk+fDkB0dDSp\nqalERUVx+PBhxowZo5XQgiAIQv3Vawz/n4Z0PvjgAwDs7Oz44osvGp5KEARB0Djx9IggCIKJEIUv\nCIJgIkThC4IgmAidPideUVEBwMmTJ8nLy9PloQVBEEzCn936Z9/+lU4L//DhwwBMnjxZl4cVBEEw\nOYcPH8bPz++OP9Np4Xfq1AmAVatW4eHhoctDC4IgmIS8vDxGjBhxu2//SqeFb2trC4CHhwdeXmI7\nOEEQBG35s2//Sty0FQRBMBGi8AVBEEyEKHxBEAQTUe/CP3v2LH369GHlypV/e+3gwYNERkYydOhQ\nFi9erJGAgiAIgmbUq/DLy8v58MMP6dq1611fj4mJITY2ljVr1hAfH09mZqZGQgqCIAgNV6/Ct7S0\nZOnSpXdd5z47OxsnJyc8PT2Ry+X07NmThIQEjQUFqKtTi41VBEEQ/qV6Tcs0NzfH3Pzub1GpVLe3\nNwRQKBRkZ2c3LN3/GPjlQTLyS2nhZo+vmx2+bva3/l5ph08jO6zMzTR6PEHQF2q1miMXr3Hk4lWK\nym5SWFZ1+39FZTcpqazG182eds1cCGvmTJi3Cy0a2YmdvIQ76HQefkNN6uvPvtMFnFOVceTiNX46\nefn2aw7W5ozo7M0L4c1ROlpLmFIQNOfqjZtsOp7DmsRLnFPdAMDO0oxGDlY0sreiuasdHZorsLcy\nv7Vfb/Jl1iTe2q/XycaCds2c6dfGg8j2Xliaizkapk5jha9UKiksLLz9tTa2OOzp70ZPf7fbX5ff\nrOG86gbnVGX8ciqfJfvPseyPCzzdrjEv9WhBS6WDRo8vCLpQV6fm0PkiVide4pe0fG7W1hHWzJl5\nkcE82tYDB2uLe773nKqME5euc/zSrU8E035M4cvfM5nUx5+nQptgJhdX/aZKY4Xv5eVFWVkZOTk5\neHh4sG/fPhYsWKCpH39XtpbmtG3iRNsmTjwV2oSsoht8c+AC645ms+5oDn1aKxnb05eOzRX3/2GC\noAeOZV3j7Y3JZBaU4WRjwYguzRjWsRmtPB7s4kUul+Hn7oCfuwNDOjZFrVbz21kVC3ad4Y11SXz5\n2zkm9/PnkUAPMdxjgmTqetwFTU1NZe7cueTm5mJubo67uzu9e/fGy8uLvn37cuTIkdsl369fP0aP\nHn3H+3NycoiIiGDv3r1aXVqhqKyKHxKy+CHhItfKqxkY1oQPn2qLnZVBjWAJJqSqppbP9mTw1e/n\naOxsw+R+/jza1hNrC83cl6qrU7MzLY+Fv5zhnOoGwV5OTHmkFd393O7/ZsGg3Ktn61X42gyiDeU3\na/jqt3PE7sukRSM7Fo8II8DDUevHFYT6SL9SwqS4k5zOK2VYx6a893gb7LV0cVJTW8ePJ3L5dE8G\nudcrGPOQD1MfDcDcTIzvG4t79axR/1u2tTTnjX6tWDW6MyWVNTz1eTxrEi+JqZ2CXqitU/Plb+d4\n8vM/KCy7ybfPdWDOoGCtlT2AuZmcwR2a8uubPRnVrTnf/HGB55YncvXGTa0dU9AfRl34f+rWshHb\nJ3SnY3MF72xKYcLak5RWVksdSzBhudcrGPJ1AnN3nqZvG3d+mdSDiNbuOju+lbkZM58MZH5kMEcu\nXuPJz//g1OUSnR1fkIZJFD6Am4MVP7zQiTf7+bMt+TJPxP5B2uViqWMJJui8qozBXx7kbF4pnw0L\nZXFUGAo7S0myDO7QlPVju1JTq2bgl/FsSbp8/zcJBstkCh9uzWB4tbcfa1/qSmV1HUO/PsSxrGtS\nxxJMSPqVEoZ8fYiqmjrixnblqdAmks+WCWnqzNbXHiK4iTMT1pxg9vZ0amrrJM0kaIdJFf6fOvko\n+PGVbjSyt+S5ZYmi9AWdOJl9nWFLDmFhJmPduK60aaw/EwjcHKxYOaYzI7t6s2T/eSasPSFK3wiZ\nZOEDeDrZsOalLqL0BZ04dL6IEUsP4WRjwbqxXfF1s5c60t9Ymsv54Km2vPdYa7an5PHWxmTq6sQE\nB2NisoUPovQF3dh3poDnliXS2NmG9eO60lTx963n9MmY7i2Y3NefTcdzmb4lVcxqMyImXfhwq/TX\nvtRVlL6gFTtSrvDSD0fxc7cnbmxX3A1knadXe7dkXE9fVh66xEc7TovSNxImX/gAHk7WovQFjUs4\nV8SEtScI9nJm9YtdJJuJ82/IZDLe7t/q9pj+Z3szpI4kaIAo/P/3v6Uv5iQLDXFeVca4lcfwdrVj\n2aiOON5jwTN9JZPJmPlEIJHtvfh0TwZL9p+TOpLQQKLw/+LP0re3MufFH45SWFYldSTBAF29cZMX\nvjuCuVzG8lEdcbIxvLL/k1wuY+6gYB4P9mT29tOsOJQldSShAUTh/w8PJ2uWjuxA0Y0qXl55jKqa\nWqkjCQakqqaWsSuOcrm4kiUjO+j9DdoHYSaX8cnQUCIClMzYnMr+syqpIwn/kij8uwjycmJ+ZAhH\nLl7j/Z/ELAXhwajVaqZuTOHIxWssHBxCe28XqSNpjIWZnNiodvi7O/DamhNkFd2QOpLwL4jC/wdP\nhDTmtd4tWXc0h2XxF6WOIxiARXsz+fFELm/28+eJkMZSx9E4W0tzlkR3AGDsimOU36yROJFQX6Lw\n72FSH38eCXRn1rZT/C4+xgr3sPlkLp/sOcugMC9e6dVS6jha08zVltjh7TibX8qUDcni06+BqXfh\nz549m6FDhzJs2DCSk5PveK13795ERUURHR1NdHQ0+fn5GgsqBblcxsdDQvF3d+DV1cc5pyqTOpKg\nh05mX2fK+mQ6+yj4aGCQ5GvjaFsPfzemPBLAtuQrfL3/vNRxhHqo18LbiYmJZGVlERcXx7lz55g2\nbRpxcXF3fM/SpUuxs7PTaEgp2VmZ881zHXjq83he/P4oP44Px8nWcGddCJpVXFHNq6uP4+ZgxdfR\n7U1mo/BxPVuQermYeTtP08bTkR7+YucsQ1Cv386EhAT69OkDgK+vL8XFxZSVGf9Vr5eLLV9Ftyf7\nWjlvrDspPsYKwK2btG9vSCavuJLPo9rhbGs4D1Y1lEwmY35ksLiJa2DqVfiFhYW4uPx35oFCoUCl\nunNse8aMGQwfPpwFCxYYVTF2bK5g2oDW7D1dwHcHL0odR9ADKw5lsTMtj7f7B9CumfHMyHlQtpbm\nfB3dHhA3cQ1Fgz5//m+hT5gwgXfeeYcVK1aQkZHBrl27GhRO34zq1pyIACUfbT8tNk8xcam5xcT8\nnE7vACWjH/KROo5kvF3tWDS8HWfyS/nPllNSxxHuo16Fr1QqKSwsvP11QUEBbm7/Hbt7+umncXV1\nxdzcnB49enD27FnNJdUDMpmM+YNDcLa14LU1J8QVjYkqrbw1bq+ws2TB4BDkcuO+SXs/Pf3deLmn\nL3FHs9mRckXqOMI91Kvww8PDb1+1p6WloVQqsbe/ta53aWkpo0eP5ubNW5shHzlyBD8/Pw3HlZ7C\nzpJPh4ZyofAGM7ekSR1H0DG1Ws20H1PJvlZBbFQ7g1oQTZsm9fUn2MuJqZtSuHy9Quo4wj+oV+GH\nhYURGBjIsGHDiImJYcaMGWzatIndu3fj4OBAjx49bk/ZVCgU9O/fX1u5JdWtZSPGP+zLuqM5Yg9Q\nE7P2SDZbky7zRl9/OjZXSB1Hb1iYyflsWDuqa+t4Y91JasXGKXpJptbhndWcnBwiIiLYu3cvXl5e\nujqsVlTX1jHk6wQy88vYPrG7UayZItxb+pUSnl4cTycfBd8/38nkh3LuZt2RbN7amMzb/QN4+WFf\nqeOYpHv1rGlMGtYCCzM5i4a1A+C1NSeoFvt/GrXK6lomrj2Bo40FHw8JFWX/DwZ38GJAkAcLfzlD\nSo6Y2KBvROE3QFOFLbMHBnEy+zof7zauG9TCnT7ZfZaz+WXMiwzGzcFK6jh6SyaTMfuZINwcrJi4\nVkxs0Dei8BvoiZDGDO3QlK9+P0fihatSxxG04MjFqyw5cJ7hnZrRq5VS6jh6z9nWko+HhHKh6AYf\n/iymauoTUfga8P4TbWjibMOUDUniisbI3KiqYfK6JLxcbHj3sdZSxzEYXX1dGdfTlzWJ2exMFVM1\n9YUofA2wtzJnfmQIWUXlzN1xWuo4ggbN3p5O9rVyFg4Oxd6qXktPmbxJffwJauLEO5tSUJWK3eP0\ngSh8Denq68qobs35PiGLg5mF93+DoPd+P6ti1eFLjHnIh04+YgpmfVmay/l4SAg3qmrFRkJ6QhS+\nBr3dPwCfRnZM2ZBMaWW11HGEBigur+atDUn4Ke2Z3K+V1HEMlp+7A5P6+rMzLY+tyWJoR2qi8DXI\nxtKMBYODuVJcwezt6VLHERpgxpZUispu8vGQUKwtzKSOY9Be7O5DaFNnpm9OpaC0Uuo4Jk0Uvoa1\n91bwYvcWrEnM5rczBVLHEf6FHSlX+OnkZV7t3ZIgLyep4xg8czM5CwaHUH6zlvd+FEM7UhKFrwWT\n+vrjp7Rn6sYUiivE0I4hKSyr4t2fUglq4mTUWxXqWkulPZP7+vPLqXyxHImEROFrgbWFGQuHhKAq\nq+I/W8UCa4ZkxuY0yiprWDgkBAsz8Z+HJo3p3oJ2zZyZvjmNghIxtCMF8RutJcFezox/2JdNx3PZ\nfcqw9/Y1FTtSrrAt5QoT+/jh7+4gdRyjYyaXsWBwCJXVtUwTQzuSEIWvRa/19iPAw4F3f0yhuFwM\n7eizazdu8v7mNAIbO/JSjxZSxzFavm72vNmvFXvS8/npZK7UcUyOKHwtsjSXMz8yhKIbN4nZJh4x\n12cf/HyK6+U3mR8phnK07YWHfGjv7cKMzWnki6EdnRK/2VoW5OXE2B4tWH8sh9/Pqu7/BkHn9qbn\n8+OJXMb3akmbxo5SxzF6ZvJbG6BX1dTxnnggS6fqXfizZ8++vclJcnLyHa8dPHiQyMhIhg4dyuLF\nizUW0tBNiPDD182OaZtSKKsSa+3ok+KKaqb9mEIrdwdeFbNydKaFmz1v9PVn96l8fhYPZOlMvQo/\nMTGRrKws4uLimDVrFrNmzbrj9ZiYGGJjY1mzZg3x8fFkZmZqNKyhsrYwY15kCJeLK8RaO3pm9rZ0\nCstuMn9wMJbm4gOvLo1+yIcQLydmbEmjqEystaML9foNT0hIoE+fPgD4+vpSXFxMWVkZANnZ2Tg5\nOeHp6YlcLqdnz54kJCRoPrGBau/twgvhPqw4lMWh80VSxxGAAxkq4o5m81KPFgR7OUsdx+SYm8mZ\nFxlCaWU1M7eKe1y6UK/CLywsxMXF5fbXCoUClerWuLRKpUKhUNz1NeGWN/u1wtvVlrc3JlNxs1bq\nOCatrKqGqRtT8HWzY2KEn9RxTFYrDwde6+3H1qTL7ErLkzqO0WvQZ1hxs6V+bCzNmDMwmKyichb+\nckbqOCZtzo50LhdXMC8yRKyVI7GXH/altacj7/2UKqYva1m9Cl+pVFJY+N+lfwsKCnBzc7vra/n5\n+SiVYneg/9XV15VnuzTj2/gLHL90Teo4JunguUJWHrrE6PBb0wMFaVmYyZkfGczVGzf5UExf1qp6\nFX54eDi7du0CIC0tDaVSib29PQBeXl6UlZWRk5NDTU0N+/btIzw8XPOJjcDUR1vT2MmGKeuTqKwW\nQzu6VH7z1lBOc1dbseyxHmnbxIlxPVuwQUxf1qp6FX5YWBiBgYEMGzaMmJgYZsyYwaZNm9i9ezcA\nM2fOZPLkyYwYMYIBAwbg4+OjldCGzt7KnI8GBnFOdYNP92RIHcekzNt5huxr5cyLDMHGUgzl6JPX\net+avvzORrGfhLbUe8+2N998846vAwICbv99x44diYuLa3gqE9DD341hHZuyZP85+rf1ILSpmCWi\nbYkXrvLdwYuM6tZc7GClh/6cvhz51UHm7DjNrGeCpI5kdMTEYwlNe6w17o7WYmhHBypu1vLWhiSa\nKmx4q78YytFX7b1dGB3uw6rDl/gjQ2wVqmmi8CXkaG3BRwODyCgoY9FeMbSjTQt/OcPFonLmDgrG\n1lJsRq7P3nykFS0a2fG2GNrROFH4Enu4lZIhHbz46vdzJGVflzqOUTqWdY1v4y8wonMzuvk2kjqO\ncB/WFmbMHxzy/1uFiifTNUkUvh5497E2KB2smbIhiaoaMbSjSZXVt4ZyGjvZ8M6A1lLHER5Qe2+X\n/98q9BL7xawdjRGFrwecbCz4aFAQZ/PF0I6mfbong3OqG8wZFIS9lRjKMSST+vrj62bH1I3JlIih\nHY0Qha8nerVSMri9F1/9fp7kHDG0ownHsq6yZP85hnVsSnc/N6njCPVkbWHGgsEh5JVUMuvndKnj\nGAVR+Hrkvcfb0Mjekinrk8XQTgPdqKphUlwSjZ1tePcxMZRjqNo1c+GlHr7EHc3mtzMFUscxeKLw\n9YiTjQVzBgZzJr+Uj3eflTqOQYvZlk72tXI+HhKKg7WF1HGEBni9jx9+SnumbkyhuEIM7TSEKHw9\n0ytASVTnZizZf56Ec2IZ5X/j19P5rEm8xEvdW4gHrIzAn0M7qrIqPvxZrLXTEKLw9dB7j7Wmuasd\nk9edFFc09XT1xk3e2pBCgIcDb/TzlzqOoCEhTZ1vr7WzM1Uso/xvicLXQ7aW5nwyNJT80iqmb06V\nOo7BUKvVvPtjCsUVN/l4SChW5mKtHGMyMcKfoCZOTN2UTF6x2Pz83xCFr6dCmzozMcKPzScvs/lk\nrtRxDMKPJ3LZkZrH5H6txGbkRsjSXM5nw0Kpqq7jjXUnqasT+3HUlyh8PTb+YV/Cmjnz3k+p5F6v\nkDqOXsu9XsGMzWl0aq7gxe4tpI4jaEkLN3tmPtmGg+eKWHLgvNRxDI4ofD1mbibnk6Gh1NWpmSyu\naP5RXZ2aN9clUadWs3BICGZymdSRBC0a0qEpj7b1YMGuM+KZlXoSha/nvF3tmPFkIIfOX+WbP8QV\nzd18vf88CeeLmP5EG5oqbKWOI2iZTCbjo4FBuDlYMXHtSW5U1UgdyWDUq/APHjxIZGQkQ4cOZfHi\nxX97PTY2ln79+hEdHU10dDTr16/XWFBTNri9F/0DPZi/6wxpl4uljqNXDp8vYsEvZ3gsyJMhHZpK\nHUfQEWdbSz4ZGsrFoht8sFVM1XxQ9Sr8mJgYYmNjWbNmDfHx8WRmZv7te0aOHMmKFStYsWIFgwcP\n1lhQU/bnFY2LrSWvrT4hloz9f4VlVby25gTNFLbMGRSETCaGckxJlxauvNzz1lO4O1KuSB3HIDxw\n4WdnZ+Pk5ISnpydyuZyePXuSkJCgzWzCX7jYWRI7vB1ZV8uZsj4Ztdq0x/Nr69RMXHuC4opqvhgR\nJp6mNVGT+voT4uXE1E0pXBYTG+7rgQtfpVKhUPz3qUWFQoFK9fdlS3fu3Mnzzz/P2LFjyc7O1kxK\nAYDOLVx559EAdqblsWS/aY/nL9qbQXxmER8+1ZbWnmIKpqmyMJPz2bB21NTW8fKq42LnuPvQ6E3b\nnj17MnHiRJYvX86TTz5JTEyMJn+8AIx+yIfHgjyZu/M0BzNNcwu4AxkqFv2awaAwLwZ38JI6jiCx\n5o3s+HhoKEnZ13nvp1ST//R7L/ct/NWrVxMdHc13331HYeF/CyY/Px+lUnnH9wYHB9OxY0cAevfu\nzdmzYgEwTZPJZMyNDMankR2vrTnBlWLT+hibV1zJ62tP4qe058OnA8W4vQDAI4EeTIjwY8OxHH5I\nyJI6jt66b+FHRUWxYsUKFi1aRFlZGTk5OdTU1LBv3z7Cw8Pv+N6YmBiOHj0KQGJiIn5+ftpJbeLs\nrcz5Oro9ldW1jF91nJs1dVJH0onq2jpeW3OciupavhjRXuxNK9zh9Qg/+rRW8uHPpzh0Xiw8eDf1\nGtKZOXMmkydPZsSIEQwYMAAfHx9UKhXTp08HYPDgwSxYsIBnn32Wb775hnfffVcroQVoqXRg/uAQ\nTly6Tsw205iWNnfHaY5cvMZHA4NoqbSXOo6gZ+RyGR8PDaWZqy2vrDounk6/C5lahwNeOTk5RERE\nsHfvXry8xNirJszadoqlBy7wydAQnmlnvP9Mf0i4yPTNaTzX1Zv/PNVW6jiCHsssKOPpxfE0b2TL\nhnHdsLYwrUX07tWz4klbA/d2/wA6+yh4Z1MKx7KuSh1HK3al5TFjSxp9Wrsz/YlAqeMIeq6l0p5P\nh4aSmlvCO5tSxE3cvxCFb+DMzeQsHhGGp5MNzy8/wum8EqkjadSxrGtMWHOCEC9nYoe3E+vkCA+k\nTxt3JvXx58cTuXz7xwWp4+gNUfhGoJG9FT+80AkbSzNGfptI9tVyqSNpxHlVGWO+P4KHkzXfPtcB\nG0vT+mguNMxrvVvySKA7s7ans/FYjtRx9IIofCPRVGHLDy90pqqmjuhvD6MqrZI6UoMUllUxavkR\nZDIZ3z/fCVd7K6kjCQZGLpfx2bB2dPN1ZcqGJLYli+UXROEbkVYeDiwb1ZH8kipGLU+kxEDX3Cm/\nWcPo745QUFrJt891oHkjO6kjCQbK2sKMpSM70N7bhYlrT7A3PV/qSJIShW9k2nu78OWzYZzJK+XF\n748a3KPm1bV1TFhzgpTcYmKHh9GumYvUkQQDZ2tpzrJRHQls7MjLK49zIOPvS8KYClH4RujhVkoW\nDgnh8IWrTFhzgppaw3gw60ZVDaO/P8qe9AL+81Rb+rZxlzqSYCQcrC34/oVOtHCz48UfjnLYRB/M\nEoVvpJ4KbcLMJ9rwy6l8xq86TvlN/d4koqisiqilh/gjQ8XcQUFEd/GWOpJgZJxtLVk5pjNNnG14\n4bsjnMw2vd2yROEbsVHhPsx4og170vMZ+vUh8ksqpY50V9lXy4n8KoHTeaV8Hd2BoR2bSR1JMFKN\n7K1YNaYLrvZWjPz2sMktwSAK38g9H+7DN8914LyqjKc+j9e7HbNOXS5h4JcHuXrjJqvGdBbDOILW\neThZs2pMZxo5WDHim8Ms++OCyTycJQrfBPQOcGf9uG7IZDD4qwT2nNKPmQoJ54oY+nUC5nIZ68d1\npUNzxf3fJAga0FRhy+ZXwokIUPLBz6eYFHeSipvST3Aorawm5udTWntuQBS+iWjT2JHNr4TTUmnP\niyuO8s2B85Jd1ajVatYmXuK5ZYl4OFmz8eVu+Ls7SJJFMF0O1hZ89Wx7Jvf1Z3PSZQZ9eVDShxYT\nL1zl0c8OsCz+Atr6L1MUvglROloT91JXHmnjQcy2dN7akMz18ps6zXCh8AZRSw8zdVMKYd7OrB/X\nlcbONjrNIAh/kstlvBbhx7JRHcm5Vs7jsX+w/6xup21W1dQyZ8dphi5JwEwuY/24bkS2185CiKLw\nTYyNpRlfjAjj1V4t2Xg8h57zf+O7+AtUa3nqZnVtHYv3ZfLIp/tJzS1m1jNtWT2mC862llo9riA8\niF6tlGx59SE8nax5bnkiH+1I18nF0Jm8Up5efJCvfj/HsI7N2D6hO+29tffsidhBwgTJ5TLefKQV\nT4Q05oOf05i59RQrD1/i/cfb0NPfTePHO5l9nakbkzmdV8qjbT2Y+WQg7o7WGj+OIDRE80Z2bBrf\njemb0/j69/OsPnSJ0d19GP2QDw7WFho9VmV1LSsSspi/6wyONuZ8+1wHIlprf8JCvQq/qqqK6dOn\nk5GRwaZNm/72emlpKZMnT6a0tBRbW1sWLlyIs7OzxsIKmtXKw4GVozuz+1Q+s7an89yyRCIClLz7\nWGtauDVsgxG1Wk1STjHrjmazJvES7g7WLIluT79ADw2lFwTNs7U0Z8HgEEY/5MMnu8/y6Z4Mvjt4\nkZd6tGBUt+YN3mXtvKqMNYmX2HAsh2vl1fRr485HA4N0tlZUvdLPmzeP1q1bk5GRcdfXv//+ezp1\n6sSYMWOIi4tj6dKlTJkyRSNBBe2QyWT0C/SgZys3vou/SOyvmfT7ZD8dmrvQw9+NHn5utPF0RP4A\nyxKr1WpOXSlha9IVtqVcJvtqBRZmMqK7eDPlkVYav0oSBG1p7enIkpEdSMkp5uPdZ5i38wzL/rjA\nCw/5EO7biABPB6zMH2z11ps1dew+lc/qxCziM4swl8t4JNCDqM7N6ObrqtN9metV+JMmTeL69ets\n2bLlrq8nJCQwe/ZsAHr16sW4ceManlDQCStzM8b29GVgmBfL4y+w74yKeTtv/aI3srfkoZaN6OHv\nhrerLVXVdVTW1N7x15xrFWxPucL5whuYyWU81LIRE3r70S/QAycbUfSCYQrycmL58504lnWNhb/c\n+u8BzmBpJqd1Y0dCvZwIbeZMUBNn1Go1+SVVFJRWUlBaRX7Jrb8ePn+VwrIqmjjbMOWRVgzu4IXS\nQZohzXoVvr29Pdev//PjyIWFhSgUt+ZSu7q6UlBQ0LB0gs65OVjxVv8A3uofQEFpJQfOFrI/Q8WB\njEJ+Onn5H98nk0EXH1fGdG9B/7YeKOzEzVjBeLT3dmH1i13IvV5BUvZ1krKvczL7OuuP5fB9QtZd\n32NvZY7SwYr23s4M69iMHv5ukm/go7Wbtqby5JoxUzpYM6i9F4Pae1FXd2u45uqNm1iZy7GyMMPa\nQo6V+a2/2luZiyEbweg1cbahibMNA4I8AaitU5NZUEZKbjGW5nKUDla4O1qjdLDCzkr/5sTcN9Hq\n1avZsWMHLi4uLFq06J7fq1QqUalUODg4kJ+fj1Kp1FhQQVpyuYy2TZykjiEIesVMLqOVhwOtPAzj\nwcH7Fn5UVBRRUVEP9MPCw8PZuXMn48eP55dffqF79+4NDigIgiBoRr0evJowYQJvvPEGFy5cIDo6\nmq1bt6JSqZg+fToA0dHRpKamEhUVxeHDhxkzZoxWQguCIAj1V69Bpn8a0vnggw8AsLOz44svvmh4\nKkEQBEHjxNIKgiAIJkIUviAIgokQhS8IgmAidDpRtLb21gYDeXl5ujysIAiCyfizX//s27/SaeGr\nVKSY5Y4AAARMSURBVLfWmR4xYoQuDysIgmByVCoV3t7ed/yZTK3DR2IrKytJTU3Fzc0NM7MHW3hI\nEARBeHC1tbWoVCratm2LtfWda/botPAFQRAE6YibtoIgCCZC/1b3+QezZ88mKSkJmUzGtGnTCA4O\nljqSTpw9e5bx48czatQonn32Wa5cucJbb71FbW0tbm5uzJ8/H0tL412Zct68eRw7doyamhrGjh1L\nUFCQyZx/RUUFU6dOpaioiKqqKsaPH09AQIDJnP+fKisrefzxxxk/fjxdu3Y1mfM/fPgwEydOxM/P\nDwB/f3/GjBnToPM3iCv8xMREsrKyiIuLY9asWcyaNUvqSDpRXl7Ohx9+SNeuXW//2aJFi4iKimL1\n6tV4e3uzYcMGCRNq16FDh8jIyCAuLo5vvvmG2bNnm9T579u3j7Zt27Jy5Uo+/fRT5syZY1Ln/6cv\nv/wSJ6dbC/eZ2vl36tSJFStWsGLFCt5///0Gn79BFH5CQgJ9+vQBwNfXl+LiYsrKyiROpX2WlpYs\nXbr0jlVHDx8+TEREBHBrk5mEhASp4mldx44d+eyzzwBwdHSkoqLCpM5/wIABvPjiiwBcuXIFd3d3\nkzp/gHPnzpGZmcnDDz8MmNbv/9009PwNovALCwtxcfnvTu4KheL2FE9jZm5u/re77BUVFbc/wrm6\nuhr1PwczMzNsbW0B2LBhAz169DCp8//TsGHDePPNN5k2bZrJnf/cuXOZOnXq7a9N7fwzMzMZN24c\nw4cPJz4+vsHnbzBj+H8lJhbdYir/HPbs2cOGDRtYtmwZ/fr1u/3npnL+a9euJT09nSlTptxxzsZ+\n/j/99BOhoaE0bdr0rq8b+/k3b96cV199lUcffZTs7GxGjhx5x8NU/+b8DaLwlUolhYWFt78uKCjA\nzc1NwkTSsbW1pbKyEmtra5PYZObAgQN89dVXfPPNNzg4OJjU+aempuLq6oqnpyetW7emtrYWOzs7\nkzn/3377jezsbH777Tfy8vKwtLQ0qX//7u7uDBgwAIBmzZrRqFEjUlJSGnT+BjGkEx4ezq5duwBI\nS0tDqVRib28vcSppdOvW7fY/C2PfZKa0tJR58+bx9ddf4+zsDJjW+R89epRly5YBt4Y1y8vLTer8\nP/30UzZu3Mi6desYPHgw48ePN6nz37JlC99++y1w66nZoqIiBg4c2KDzN5gHrxYsWMDRo0eRyWTM\nmDGDgIAAqSNpXWpqKnPnziU3Nxdzc3Pc3d1ZsGABU6dOpaqqisaNG/PRRx9hYWGce8nGxcURGxuL\nj4/P7T+bM2cO7733nkmcf2VlJe/+Xzt2aANACARRdPqgAhJKoKSVJAjKoEg8QZBgzp5Hkf2vgzFf\nTGsaY2jvLTNTSkm1Vhf7/3rvCiEo5+xm/1pLpRTNOXXOkZkpxni1/5ngAwDuPHHpAADuEXwAcILg\nA4ATBB8AnCD4AOAEwQcAJwg+ADhB8AHAiQ/2qfmuHom4xQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f5c0631ef98>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "ax1 = fig.add_axes([0.1, 0.5, 0.8, 0.4],\n",
    "                   xticklabels=[], ylim=(-1.2, 1.2))\n",
    "ax2 = fig.add_axes([0.1, 0.1, 0.8, 0.4],\n",
    "                   ylim=(-1.2, 1.2))\n",
    "\n",
    "x = np.linspace(0, 10)\n",
    "ax1.plot(np.sin(x))\n",
    "ax2.plot(np.cos(x));"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We now have two axes (the top with no tick labels) that are just touching: the bottom of the upper panel (at position 0.5) matches the top of the lower panel (at position 0.1 + 0.4)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## ``plt.subplot``: Simple Grids of Subplots\n",
    "\n",
    "Aligned columns or rows of subplots are a common-enough need that Matplotlib has several convenience routines that make them easy to create.\n",
    "The lowest level of these is ``plt.subplot()``, which creates a single subplot within a grid.\n",
    "As you can see, this command takes three integer arguments—the number of rows, the number of columns, and the index of the plot to be created in this scheme, which runs from the upper left to the bottom right:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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CiMLCQjF58mQxfvx4sXz5cqu2/fv3C7VaLQ4dOuTSOe7lC1+q8sVcU1NTO2Ro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\nnohIBljsiYhk4P8BcOyRN6ZCZI4AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f5c306af208>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for i in range(1, 7):\n",
    "    plt.subplot(2, 3, i)\n",
    "    plt.text(0.5, 0.5, str((2, 3, i)),\n",
    "             fontsize=18, ha='center')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The command ``plt.subplots_adjust`` can be used to adjust the spacing between these plots.\n",
    "The following code uses the equivalent object-oriented command, ``fig.add_subplot()``:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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+f/755zh06BDy8vKgUCis5k+dOhWDBg1CUVGRw/voTnw9b1uY97/4atalpaVI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\n/wO9PuOW7CYrvgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f5c0611f4e0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "fig.subplots_adjust(hspace=0.4, wspace=0.4)\n",
    "for i in range(1, 7):\n",
    "    ax = fig.add_subplot(2, 3, i)\n",
    "    ax.text(0.5, 0.5, str((2, 3, i)),\n",
    "           fontsize=18, ha='center')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We've used the ``hspace`` and ``wspace`` arguments of ``plt.subplots_adjust``, which specify the spacing along the height and width of the figure, in units of the subplot size (in this case, the space is 40% of the subplot width and height)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## ``plt.subplots``: The Whole Grid in One Go\n",
    "\n",
    "The approach just described can become quite tedious when creating a large grid of subplots, especially if you'd like to hide the x- and y-axis labels on the inner plots.\n",
    "For this purpose, ``plt.subplots()`` is the easier tool to use (note the ``s`` at the end of ``subplots``). Rather than creating a single subplot, this function creates a full grid of subplots in a single line, returning them in a NumPy array.\n",
    "The arguments are the number of rows and number of columns, along with optional keywords ``sharex`` and ``sharey``, which allow you to specify the relationships between different axes.\n",
    "\n",
    "Here we'll create a $2 \\times 3$ grid of subplots, where all axes in the same row share their y-axis scale, and all axes in the same column share their x-axis scale:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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cx9Hc3Jz8fn8+6wYAZCHtNk5DQ4Nqa2sVDAZlWZbC4bAGBwdVVlam1tZWSf8O9J07d7pr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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f5c0603f4a8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(2, 3, sharex='col', sharey='row')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that by specifying ``sharex`` and ``sharey``, we've automatically removed inner labels on the grid to make the plot cleaner.\n",
    "The resulting grid of axes instances is returned within a NumPy array, allowing for convenient specification of the desired axes using standard array indexing notation:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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pQUpKCgCgpaUFOTk5yMrKQk1Nja/nTUREbnDpnv1AgiAMauvs7EROTg50Oh3Cw8Mxffp0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\nRK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f5c0603f4a8>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# axes are in a two-dimensional array, indexed by [row, col]\n",
    "for i in range(2):\n",
    "    for j in range(3):\n",
    "        ax[i, j].text(0.5, 0.5, str((i, j)),\n",
    "                      fontsize=18, ha='center')\n",
    "fig"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In comparison to ``plt.subplot()``, ``plt.subplots()`` is more consistent with Python's conventional 0-based indexing."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## ``plt.GridSpec``: More Complicated Arrangements\n",
    "\n",
    "To go beyond a regular grid to subplots that span multiple rows and columns, ``plt.GridSpec()`` is the best tool.\n",
    "The ``plt.GridSpec()`` object does not create a plot by itself; it is simply a convenient interface that is recognized by the ``plt.subplot()`` command.\n",
    "For example, a gridspec for a grid of two rows and three columns with some specified width and height space looks like this:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "grid = plt.GridSpec(2, 3, wspace=0.4, hspace=0.3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From this we can specify subplot locations and extents using the familiary Python slicing syntax:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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fnx4xozLXz+98WWutNQ6Hw9i3bx8OHTqEkpISPSKqImW9cuUKhoeHUVNTgwMHDqC3txdN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\nAbDsiYgEwLInIhIAy56ISAAseyIiAfwfQ96po68cdJkAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f5c05e97c50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.subplot(grid[0, 0])\n",
    "plt.subplot(grid[0, 1:])\n",
    "plt.subplot(grid[1, :2])\n",
    "plt.subplot(grid[1, 2]);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This type of flexible grid alignment has a wide range of uses.\n",
    "I most often use it when creating multi-axes histogram plots like the ones shown here:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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6VcbGxoAH/eovf/nLwl44MzNDNBolFovh8/koFovAg0+VFy5cEHGxj+P0eFQV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\ntnhj54yJyAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f5c05b8c4e0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create some normally distributed data\n",
    "mean = [0, 0]\n",
    "cov = [[1, 1], [1, 2]]\n",
    "x, y = np.random.multivariate_normal(mean, cov, 3000).T\n",
    "\n",
    "# Set up the axes with gridspec\n",
    "fig = plt.figure(figsize=(6, 6))\n",
    "grid = plt.GridSpec(4, 4, hspace=0.2, wspace=0.2)\n",
    "main_ax = fig.add_subplot(grid[:-1, 1:])\n",
    "y_hist = fig.add_subplot(grid[:-1, 0], xticklabels=[], sharey=main_ax)\n",
    "x_hist = fig.add_subplot(grid[-1, 1:], yticklabels=[], sharex=main_ax)\n",
    "\n",
    "# scatter points on the main axes\n",
    "main_ax.plot(x, y, 'ok', markersize=3, alpha=0.2)\n",
    "\n",
    "# histogram on the attached axes\n",
    "x_hist.hist(x, 40, histtype='stepfilled',\n",
    "            orientation='vertical', color='gray')\n",
    "x_hist.invert_yaxis()\n",
    "\n",
    "y_hist.hist(y, 40, histtype='stepfilled',\n",
    "            orientation='horizontal', color='gray')\n",
    "y_hist.invert_xaxis()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This type of distribution plotted alongside its margins is common enough that it has its own plotting API in the Seaborn package; see [Visualization With Seaborn](04.14-Visualization-With-Seaborn.ipynb) for more details."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<!--NAVIGATION-->\n",
    "< [Customizing Colorbars](04.07-Customizing-Colorbars.ipynb) | [Contents](Index.ipynb) | [Text and Annotation](04.09-Text-and-Annotation.ipynb) >"
   ]
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}
